skip to main content
10.1145/3485447.3512184acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

A Rapid Source Localization Method in the Early Stage of Large-scale Network Propagation

Published: 25 April 2022 Publication History

Abstract

Recently, the rapid diffusion of malicious information in online social networks causes great harm to our society. Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. More specifically, GFNL includes two main components, i.e., the greedy-based sensor deployment strategy (DS) and direction-path-based source estimation strategy (ES). In more detail, to ensure sensors can observe a propagation information as early as possible, a set of sensors is deployed in a network to minimize the geodesic distance (i.e., the distance of the shortest path) between the candidate set and the sensor set based on DS. Then when a fraction of sensors observe a propagation, ES infers the source based on the idea that the distance of the actual propagation path is proportional to the observed time. Compared with some state-of-the-art methods, comprehensive experiments have proved the superiority and robustness of our proposed GFNL.

References

[1]
Ameya Agaskar and Yue M Lu. 2013. A fast Monte Carlo algorithm for source localization on graphs. In Wavelets and Sparsity XV. SPIE, San Diego, CA, United States, 88581N.
[2]
Jonathan Berry, William E Hart, Cynthia A Phillips, James G Uber, and Jean-Paul Watson. 2006. Sensor placement in municipal water networks with temporal integer programming models. Journal of Water Resources Planning and Management 132, 4(2006), 218–224.
[3]
Phillip Bonacich. 2007. Some unique properties of eigenvector centrality. Social Networks 29, 4 (2007), 555–564.
[4]
Ulrik Brandes. 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 2 (2001), 163–177.
[5]
Ming Dong, Bolong Zheng, Nguyen Quoc Viet Hung, Han Su, and Guohui Li. 2019. Multiple rumor source detection with graph convolutional networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 569–578.
[6]
Glenn Ellison. 2020. Implications of heterogeneous SIR models for analyses of COVID-19. Technical Report. National Bureau of Economic Research.
[7]
Chao Gao and Jiming Liu. 2016. Network-based modeling for characterizing human collective behaviors during extreme events. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 1(2016), 171–183.
[8]
Chao Gao, Zhen Su, Jiming Liu, and Jürgen Kurths. 2019. Even central users do not always drive information diffusion. Commun. ACM 62, 2 (2019), 61–67.
[9]
Pablo M Gleiser and Leon Danon. 2003. Community structure in jazz. Advances in Complex Systems 6, 04 (2003), 565–573.
[10]
Andrew C Hayward, Ellen B Fragaszy, Alison Bermingham, Lili Wang, Andrew Copas, W John Edmunds, Neil Ferguson, Nilu Goonetilleke, Gabrielle Harvey, Jana Kovar, 2014. Comparative community burden and severity of seasonal and pandemic influenza: results of the Flu Watch cohort study. The Lancet Respiratory Medicine 2, 6 (2014), 445–454.
[11]
Qiangjuan Huang. 2017. Source locating of spreading dynamics in temporal networks. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, Perth, WA, Australia, 723–727.
[12]
Flavio Iannelli, Andreas Koher, Dirk Brockmann, Philipp Hövel, and Igor M Sokolov. 2017. Effective distances for epidemics spreading on complex networks. Physical Review E 95, 1 (2017), 012313.
[13]
Jiaojiao Jiang, Sheng Wen, Bo Liu, Shui Yu, Yang Xiang, and Wanlei Zhou. 2019. Malicious attack propagation and source identification. Springer, Gewerbestrasse 11, 6330 Cham, Switzerland.
[14]
Jiaojiao Jiang, Sheng Wen, Shui Yu, Yang Xiang, and Wanlei Zhou. 2016. Identifying propagation sources in networks: State-of-the-art and comparative studies. IEEE Communications Surveys & Tutorials 19, 1 (2016), 465–481.
[15]
Brian Karrer and Mark EJ Newman. 2010. Message passing approach for general epidemic models. Physical Review E 82, 1 (2010), 016101.
[16]
William Ogilvy Kermack and Anderson G McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115, 772 (1927), 700–721.
[17]
William Ogilvy Kermack and Anderson G McKendrick. 1932. Contributions to the mathematical theory of epidemics. II.—The problem of endemicity. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 138, 834 (1932), 55–83.
[18]
Christian Kreibich and Jon Crowcroft. 2004. Honeycomb: creating intrusion detection signatures using honeypots. ACM SIGCOMM Computer Communication Review 34, 1 (2004), 51–56.
[19]
Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th International Conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, 641–650.
[20]
Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. NEW YORK, NY 10036-9998 USA, Atlanta, GA, 1361–1370.
[21]
Jure Leskovec and Julian J Mcauley. 2012. Learning to discover social circles in ego networks. In Advances in Neural Information Processing Systems. Neural information processing systems foundation, Lake Tahoe, NV, United states, 539–547.
[22]
Yuxin Liu, Chao Gao, Xinyan She, and Zili Zhang. 2016. A bio-inspired method for locating the diffusion source with limited observers. In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, Vancouver, BC, Canada, 508–514.
[23]
David E Losada, Fabio Crestani, and Javier Parapar. 2017. eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Dublin, Ireland, 346–360.
[24]
Seth A Myers, Chenguang Zhu, and Jure Leskovec. 2012. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, 33–41.
[25]
World Health Organization 2020. Novel Coronavirus (2019-nCoV): Situation report, 11. Technical Report. World Health Organization.
[26]
Robert Paluch, Łukasz Gajewski, Krzysztof Suchecki, Bolesław Szymański, and Janusz A Hołyst. 2021. Enhancing maximum likelihood estimation of infection source localization. In Simplicity of Complexity in Economic and Social Systems. Springer, Cham, 21–41.
[27]
Robert Paluch, Łukasz G Gajewski, Janusz A Hołyst, and Boleslaw K Szymanski. 2020. Optimizing sensors placement in complex networks for localization of hidden signal source: A review. Future Generation Computer Systems 112 (2020), 1070–1092.
[28]
Robert Paluch, Xiaoyan Lu, Krzysztof Suchecki, Bolesław K Szymański, and Janusz A Hołyst. 2018. Fast and accurate detection of spread source in large complex networks. Scientific Reports 8, 1 (2018), 1–10.
[29]
Pedro C Pinto, Patrick Thiran, and Martin Vetterli. 2012. Locating the source of diffusion in large-scale networks. Physical Review Letters 109, 6 (2012), 068702.
[30]
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. 2021. Multi-scale attributed node embedding. Journal of Complex Networks 9, 2 (2021), 1–22.
[31]
Zhesi Shen, Wen-Xu Wang, Ying Fan, Zengru Di, and Ying-Cheng Lai. 2014. Reconstructing propagation networks with natural diversity and identifying hidden sources. Nature Communications 5, 1 (2014), 1–10.
[32]
Zhen Su, Fanzhen Liu, Chao Gao, Shupeng Gao, and Xianghua Li. 2018. Inferring infection rate based on observations in complex networks. Chaos, Solitons & Fractals 107 (2018), 170–176.
[33]
Wenchang Tang, Feng Ji, and Wee Peng Tay. 2018. Estimating infection sources in networks using partial timestamps. IEEE Transactions on Information Forensics and Security 13, 12(2018), 3035–3049.
[34]
Hongjue Wang and Kaijia Sun. 2020. Locating source of heterogeneous propagation model by universal algorithm. EPL (Europhysics Letters) 131, 4 (2020), 48001.
[35]
Zheng Wang, Chaokun Wang, Jisheng Pei, and Xiaojun Ye. 2017. Multiple source detection without knowing the underlying propagation model. In Proceedings of the AAAI Conference on Artificial Intelligence. PALO ALTO, CA 94303 USA, San Francisco, CA, 217–223.
[36]
Fan Yang, Shuhong Yang, Yong Peng, Yabing Yao, Zhiwen Wang, Houjun Li, Jingxian Liu, Ruisheng Zhang, and Chungui Li. 2020. Locating the propagation source in complex networks with a direction-induced search based Gaussian estimator. Knowledge-Based Systems 195 (2020), 105674.
[37]
Wenyu Zang, Chuan Zhou, Li Guo, and Peng Zhang. 2015. Topic-aware source locating in social networks. In Proceedings of the 24th International Conference on World Wide Web. NEW YORK, NY 10036-9998 USA, Florence, ITALY, 141–142.
[38]
Yubo Zhang, Xizhe Zhang, and Bin Zhang. 2015. Analysis of accuracy of the locating information source method based on observers. Journal of Northeastern University 36, 3 (2015), 350–353.
[39]
Kai Zhu, Zhen Chen, and Lei Ying. 2017. Catch’em all: Locating multiple diffusion sources in networks with partial observations. In Thirty-First AAAI Conference on Artificial Intelligence. PALO ALTO, CA 94303 USA, San Francisco, CA, 1676–1682.
[40]
Kai Zhu and Lei Ying. 2014. Information source detection in the SIR model: A sample-path-based approach. IEEE/ACM Transactions on Networking 24, 1 (2014), 408–421.

Cited By

View all
  • (2025)Indirect information propagation model with time-delay effect on multiplex networksChaos, Solitons & Fractals10.1016/j.chaos.2024.115936192(115936)Online publication date: Mar-2025
  • (2024)Joint source localization in different platforms via implicit propagation characteristics of similar topicsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/268(2424-2432)Online publication date: 3-Aug-2024
  • (2024)New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation ScenariosProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679796(839-848)Online publication date: 21-Oct-2024
  • Show More Cited By

Index Terms

  1. A Rapid Source Localization Method in the Early Stage of Large-scale Network Propagation
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      WWW '22: Proceedings of the ACM Web Conference 2022
      April 2022
      3764 pages
      ISBN:9781450390965
      DOI:10.1145/3485447
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 April 2022

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. network propagation
      2. social network dynamics
      3. source localization

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Funding Sources

      Conference

      WWW '22
      Sponsor:
      WWW '22: The ACM Web Conference 2022
      April 25 - 29, 2022
      Virtual Event, Lyon, France

      Acceptance Rates

      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)110
      • Downloads (Last 6 weeks)8
      Reflects downloads up to 30 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2025)Indirect information propagation model with time-delay effect on multiplex networksChaos, Solitons & Fractals10.1016/j.chaos.2024.115936192(115936)Online publication date: Mar-2025
      • (2024)Joint source localization in different platforms via implicit propagation characteristics of similar topicsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/268(2424-2432)Online publication date: 3-Aug-2024
      • (2024)New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation ScenariosProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679796(839-848)Online publication date: 21-Oct-2024
      • (2024)Path-Wise Continuous-Time Transmission with Applications in Source Identification from Partial ObservationsInternational Journal of Modern Physics C10.1142/S0129183124502097Online publication date: 26-Jul-2024
      • (2024)MASE: Multi-Attribute Source Estimator for Epidemic Transmission in Complex NetworksIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.334953754:6(3308-3320)Online publication date: Jun-2024
      • (2024)Complex Continuous Action Iterated Dilemma With Incremental Dynamic ModelIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.334494254:4(2309-2319)Online publication date: Apr-2024
      • (2024)Random Full-Order-Coverage Based Rapid Source Localization With Limited Observations for Large-Scale NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.340639411:5(4213-4226)Online publication date: Sep-2024
      • (2024)Multi-Feature Rumor Suppression Mechanism Based on Community Division in Social NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.333664911:2(2047-2061)Online publication date: Mar-2024
      • (2024)Belief Rényi Divergence of Divergence and its Application in Time Series ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336971936:8(3670-3681)Online publication date: Aug-2024
      • (2024)Fractal Belief Rényi Divergence With its Applications in Pattern ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334290736:12(8297-8312)Online publication date: Dec-2024
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media